Anatomical data

Author

Anastasios Dadiotis

Visual Reports MRIQC unprocessed T1w data

In this document we will make notes on how to interpret the visual reports of MRIQC specifically for T1w data.

Common artifacts with bold data

For:

  1. Artifactual structures in the background

  2. Susceptibility distortion artifacts

    1. Signal drop-out
    2. Brain distortions

  3. Aliasing ghost

  4. Wrap-around that overlaps with the brain

  5. Data formatting issues

  • Description is the same as the BOLD reports.

  • Note that normalization and co-registration are relative robust to structural images with mild artifacts, therefore it is not always absolute necessity to impose exclusion criteria on the unprocessed T1w images.

Examples:

Motion - related and Gibbs ringing:

Large head motion during the acquisition of T1w images often expresses itself with the appearance of concentric ripples throughout the scan

  • Gibbs ringing: is a consequence of the truncation of the Fourier series approximation and appears as multiple fine lines immediately adjacent and parallel to high contrast interfaces. But it is the same with subtle cases of motion related issues.

  • The ripples cause by motion generally span the whole brain and are primarily visible in the sagittal view fo MRIQC’s mosaic view

For an example see the above image: Figure S10E

Intensity non-uniformity:

  • Is characterized by a smooth variation (low spatial frequency) of intensity throughout the brain caused by the strongel signal sensed in the proximity of coils.

    • Where: On the zoomed-in view on the T1w image
    • Can be a problem for automated processing methods that assume a type of tissue [e.g. white matter (WM)] is represented by voxels of similar intensities across the whole brain.
    • An extreme intensity non-uniformity can also be a sign of coil failure

For an example see the above image Figure S10F

Eye spillover

  • Eye movements may trigger the signal leakage from the eyes through the imaging axes with the lowest bandwidth (i.e., acquired faster), potentially overlapping signal from brain tissue.

    • A strong signal leakage can however be noticeable on the zoomed-in view of the T1w image
    • In defaced data the leakage might not be visible

For an example see the above image Figure S10G

Other Notes on Interpretation

Interpretation of the visual reports of the T1w images based on (“Introduction to MRIQC [TRAIN-05-2022] - YouTube,” n.d.)

Zoomed-in mosaic view of the brain

  • Zoomed in brain masks of the T1w images in a horzontal view

    • We want high contrast between the grey matter and the white matter

    • We also have to check for ringing artifacts

Background noise

  • Yellow and green are areas with high background noise

  • Dark purple are areas with lwo background noise

Areas like:

  1. The teeth

  2. Sinus cavities

  3. Air canals

  • Are areas with high background noise

Example:

But: whenever you get in the brain you don’t really want any noise, you want that all purple

Example:

And same thing for the saggital view

Example:

(Note: After discussing with Gaëlle, the above examples are super super clean, and is not that usual to actually get so clean data, it’s ok).

References

“Introduction to MRIQC [TRAIN-05-2022] - YouTube.” n.d. https://www.youtube.com/watch?v=In6Dez_uuxQ&t=559s&ab_channel=UABResearchComputing.